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Optimisation de trajectoires pour l’évitement des zones de Turbulence en Air Clair en prenant compte de leurs incertitudes
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Magnitude
No full textMagnitude is a simulation tool prototype, designed by Laetitia Bornes, as part of her thesis, and developed by Laetitia Bornes, Mathieu Magnaudet, Stéphane Conversy, and Mathieu Poirier. This tool allows, among other things, the modelling of direct and indirect environmental impacts of digital products or services. It was designed to enable collaborative construction of a simplified model of direct and indirect effects of an intervention (product, service, regulation, etc.) based on mixed data (system data, user surveys, expert assumptions). The objective is to enable involved stakeholders (decision-makers, designers, policymakers, citizens, etc.) to better understand the dynamics of these impacts by interacting with the model, and to identify and test mitigation strategies. Comparing multiple simulations (representing various scenarios and strategies) helps identify realistic and impactful lever combinations
Formally Verified Hardening of C Programs against Hardware Fault Injection
No full textInternational audienceA fault attack is a malicious manipulation of the hardware (e.g., electromagnetic or laser pulse) that modifies the behavior of the software. Fault attacks typically target sensitive applications such as cryptography services, authentication, boot-loaders or firmware updaters. They can be defended against by adding countermeasures, that is, control flow checks and redundancies, either in the hardware, or in the software running on it. In particular, software countermeasures may be added automatically during compilation.In this paper, we describe a formally verified implementation of this approach in the CompCert verified compiler for the C language. We implemented two existing countermeasures protecting the control flow of the program as program transformations over a middle-end intermediate representation of CompCert, RTL. We proved that these countermeasures are correct, that is, they do not change the observable behavior of the program during an execution without fault injection. We then modeled the effect of a fault on the behavior of the program as an extension of the semantic model of RTL. We used this new model to formally prove the efficacy of the countermeasure: all attacks are either caught, or produce no observable effects. In addition to this formal reasoning, we evaluated the protected program using Lazart, a tool for symbolic fault injection, and measured the effect of optimizations on security and performance
Impact de l'ajustement dynamique de la difficulté du jeu sur le sentiment de flow
No full textInternational audienceDynamic difficulty adjustment (DDA) is a key mechanism for maintaining player engagement in video games. This study compares two types of DDA: one based on player performance, and the other on pupil size, a physiological indicator reflecting arousal and cognitive load. Forty-four participants played two sessions of Tetris using these two adjustment methods. Flow experience was measured using a standardized scale and an innovative temporal graphtracing method. Although no significant difference was observed on average in flow scores, exploratory results show that pupil-based adjustment leads to more frequent changes in game difficulty and a generally higher effective level of challenge, without degrading performance or the players' flow experience. These results highlight the potential of difficulty adjustments based on the player's physiological state and open up new perspectives for affective game design
Hybrid method for holistic air traffic demand and capacity balancing optimisation based on sector complexity
No full textInternational audienceThis paper presents a new hybrid method, based on simulated annealing and dynamic programming, tailored to solve a Demand and Capacity Balancing (DCB) problem that overcomes the limitations of the current Air Traffic Flow and Capacity Management (ATFCM) system by: (a) the introduction of complexity metrics (instead of entry counts) in order to measure the traffic load; (b) the better consideration of the airspace users' preferences, allowing the possibility of submitting alternative trajectories to avoid congested airspace; and (c) the holistic integration of the demand and capacity management into the same optimisation problem. This new method is compared with the state-of-the-art method for MILP providing better performance principally when the difficulty of the problem increases. Finally, the proposed method is applied to a real-scale scenario, demonstrating its practical applicability in real-world cases.</div
Optimized Area Coverage in Disaster Response Utilizing Autonomous UAV Swarm Formations
No full textInternational audienceThis paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends flight duration and enhances data availability, reducing the risk of mission failure due to collisions. To mitigate this risk further, we introduce an autonomous navigation framework that utilizes a local Euclidean Signed Distance Field (ESDF) map for obstacle avoidance while maintaining swarm formation with minimal path deviation. Additionally, we incorporate a Traveling Salesman Problem (TSP) variant to optimize area coverage, prioritizing Points of Interest (POIs) based on preassigned values derived from environmental behavior and critical infrastructure. The proposed system is validated through simulations with varying swarm sizes, demonstrating its ability to maximize coverage while ensuring collision avoidance between UAVs and obstacles
Optimisation des architectures systèmes : modélisations fonctionnelles, optimisations algorithmiques et évaluations multidisciplinaires
No full textWhen designing complex systems, choices related to the system architecture, the description of the functions and components of a system, greatly influence to which extent design goals can be achieved. The architecture design space, the set of all possible architectures for a given design problem, can be extremely large due to the combinatorial nature of architectural choices. Additionally, the integration of innovative technologies for future systems requires the application of multidisciplinary, simulation-based evaluation. These two challenges are addressed by System Architecture Optimization (SAO): the combination of numerical optimization algorithms with multidisciplinary, simulation-based evaluation, to explore architecture design spaces without requiring the evaluation of all architectures.A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed.Lors de la conception de systèmes complexes, les choix relatifs à l’architecture — c’est‑à‑dire la description des fonctions et des composants d’un système — influencent fortement la mesure dans laquelle les objectifs de conception peuvent être atteints. L’espace de conception architecturale, ensemble de toutes les architectures possibles pour un problème donné, peut être extrêmement vaste en raison de la nature combinatoire des choix architecturaux. De plus, l’intégration de technologies innovantes pour les systèmes futurs nécessite des évaluations multidisciplinaires basées sur la simulation. Ces deux défis sont relevés par l’Optimisation de l’Architecture Système (SAO) : la combinaison d’algorithmes d’optimisation numérique et d’évaluations multidisciplinaires basées sur la simulation, permettant d’explorer l’espace de conception architecturale sans avoir à évaluer toutes les architectures.Une méthode fondée sur les fonctions est développée pour modéliser les problèmes SAO. Le Graphique de l’Espace de Conception Architecturale (ADSG) est mis au point pour représenter l’allocation fonction‑composant, la caractérisation des composants et les choix de connexion entre composants. Des algorithmes permettent d’encoder automatiquement un ADSG en un problème d’optimisation numérique et de décoder les vecteurs de conception générés en instances d’architecture. Cette méthode de modélisation est accessible via une application Web dotée d’une interface graphique.Les problèmes SAO sont résolus à l’aide d’algorithmes évolutifs et d’Optimisation Bayésienne (BO). Un nouvel algorithme d’échantillonnage hiérarchique est développé pour éviter la sous‑ ou la sur‑échantillonnage de certaines régions de l’espace de conception lors de la construction du plan d’expériences initial (DoE). Des études explorent les algorithmes de correction et l’exploitation de l’information hiérarchique de l’espace de conception. Pour l’optimisation bayésienne, une stratégie est mise en place pour gérer les contraintes cachées liées aux échecs de simulation. Il est démontré que les algorithmes évolutifs et bayésiens peuvent résoudre les problèmes SAO, mais que l’optimisation bayésienne y parvient avec 92 % d’évaluations de fonctions en moins, comme le montre un cas réaliste.L’évaluation multidisciplinaire basée sur la simulation dans de vastes projets d’ingénierie système inter‑organisationnels est rendue possible grâce à la conception et à l’optimisation multidisciplinaires de concepts (MDAO). Des méthodes sont développées pour modifier automatiquement le comportement d’un flux de travail MDAO collaboratif pour chaque architecture système évaluée, pour propager toutes les informations sur l’architecture vers le Schéma de Données Central (CDS) utilisé en MDAO collaboratif, et pour exécuter le processus de génération d’architecture dans l’environnement informatique où le flux de travail est lancé
Remise de prix science ouverte de la thèse, édition 2025: Catégorie « Sciences et technologies »
No full textInternational audiencePaul Saves – Optimisation pour l’éco-conception avion-----Titre de la thèse : « Optimisation multi-disciplinaire en grande dimension pour l’éco-conception avion en avant-projet »Thèse soutenue en 2024, dans le domaine des Mathématiques et Applications, à l’Institut supérieur de l’aéronautique et de l’espace de Toulouse
Collaborative strategic conflict management for 4D trajectories under weather forecast uncertainty
No full textInternational audienceThe design of decision support tools for strategic conflict management (SCM) needs to integrate and manage uncertainty while accommodating the diverse performance preferences of multiple stakeholders. This paper proposes a novel collaborative SCM approach for four-dimensional (4D) trajectories under weather forecast uncertainty, integrating trajectory prediction, strategic conflict detection and resolution, and collaborative decision-making. A 4D grid-based conflict risk assessment method is introduced for trajectories generated by the ensemble trajectory predictor, incorporating weather uncertainty from ensemble forecasts. A multi-objective optimization model is formulated to reorganize aircraft trajectories within free route airspace, employing rerouting, flight level allocation, and speed control to optimize safety, efficiency, and predictability. Predictability is explicitly considered to enhance adherence to planned trajectories and reduce operational uncertainty, while equity is incorporated as a constraint to ensure a fair distribution of trajectory adjustments. To efficiently solve this large-scale multi-objective SCM problem, a decomposition-based memetic algorithm (DMA) is proposed. The DMA combines a decomposition-based global search framework with local refinement via a hybridization strategy to achieve a good balance between exploration and exploitation. The effectiveness of the proposed method is validated using a simulation scenario featuring 760 flights in the high-density western China airspace. Results demonstrate that the approach effectively identifies trade-offs between different stakeholder objectives and provides optimized solutions that support collaborative decision-making in strategic conflict management
Major Air Traffic Flow Identification with Fractal-Based Graph Simplification
No full textInternational audienceMajor air traffic flows are concentrated streams of flights that follow similar trajectories between specific geographical regions or airport pairs, and they play a key role in tasks such as workload evaluation and demand-capacity balancing. However, real-world flows exhibit strong interconnectivity, including merging, overlapping, and splitting, which makes flow boundaries difficult to distinguish. This complexity limits traditional methods based on trajectory similarity or predefined origin-destination pairs, which rely on a top-down perspective to directly extract flow segments from the global traffic structure. This paper reconceptualizes major flow identification as a bottom-up process. Major flows are identified by deriving and consolidating local flow structures, thereby capturing flow interconnectivity to offer a more interpretable and robust representation. First, flowshaping areas are detected through density-based clustering of trajectory waypoints, capturing potential origins, terminations, and transit points. Second, local flow trees are constructed through fractal-based simplifications to preserve the essential flow hierarchy while smoothing small-scale deviations. Third, a stability metric is introduced to extract major flows that exhibit both high traffic volume and spatial persistence. Finally, an optimization framework consolidates these local major flows into a coherent set of global major flows across the airspace. The methodology is tested on northwest-southwest traffic in European airspace with flight plan data on 14th July 2023. Results show that the majority of flights can be organized into major flows, yielding stable representations of dominant traffic corridors. In addition, the framework highlights alternative routing options and abnormal trajectory patterns, supporting applications in traffic flow management, re-routing, and anomaly detection.</div